Surround-view camera system (vpm) and vehicle dynamic

ABSTRACT

A system and method for correcting the calibration of a plurality of cameras on a mobile platform such as in a surround-view camera system on a vehicle based on changes in vehicle dynamics. The method includes reading measurement values from one or more sensors on the vehicle that identify a change in vehicle dynamics and defining the plurality of cameras and a vehicle body as a single reference coordinate system. The method also includes identifying the measured values as a rotation matrix and a translation vector in the coordinate system, and integrating the rotation matrix and the translation vector into a relationship between a vehicle coordinate system and a camera coordinate system to provide the calibration correction of the cameras.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of the priority date of U.S.Provisional Patent Application Ser. No. 61/994,686, titled,Surround-View Camera System (VPM) and Vehicle Dynamic, filed May 16,2014.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to a system and method for correctingthe calibration of a plurality of cameras on a mobile platform and, moreparticularly, to a system and method for correcting the calibration of aplurality of cameras in a surround-view camera system on a vehicle thatincludes providing corrections for vehicle dynamics as detected bysensors on the vehicle.

2. Discussion of the Related Art

Modern vehicles generally include one or more cameras that provideback-up assistance, take images of the vehicle driver to determinedriver drowsiness or attentiveness, provide images of the road as thevehicle is traveling for collision avoidance purposes, provide structurerecognition, such as roadway signs, etc. Other vehicle visionapplications include vehicle lane sensing systems to sense the vehicletravel lane and drive the vehicle in the lane-center. Many of theseknown lane sensing systems detect lane-markers on the road for variousapplications, such as lane departure warning (LDW), lane keeping (LK),lane centering (LC), etc., and have typically employed a single camera,either at the front or rear of the vehicle, to provide the images thatare used to detect the lane-markers.

It has been proposed in the art to provide a surround-view camera systemon a vehicle that includes a front camera, a rear camera and left andright side cameras, where the camera system generates a top-down view ofthe vehicle and surrounding areas using the images from the cameras, andwhere the images overlap each other at the corners of the vehicle. Thetop-down view can be displayed for the vehicle driver to see what issurrounding the vehicle for back-up, parking, etc. Future vehicles maynot employ rearview mirrors, but may instead include digital imagesprovided by the surround view cameras.

U.S. Patent Application Publication No. 2013/0293717 to Zhang et al.,filed Apr. 9, 2013, titled, Full Speed Lane Sensing With A SurroundingView System, assigned to the assignee of this application and hereinincorporated by reference, discloses a system and method for providinglane sensing on a vehicle by detecting roadway lane-markers, where thesystem employs a surround-view camera system providing a top-down viewimage around the vehicle. The method includes detecting left-side andright-side lane boundary lines in the top-down view image, and thendetermining whether the lane boundary lines in the image are alignedfrom image frame to a next image frame and are aligned from image toimage in the top-down view image.

For many camera-based vehicle applications it is critical to accuratelycalibrate the position and orientation of the camera with respect to thevehicle. Camera calibration generally refers to estimating a number ofcamera parameters including both intrinsic and extrinsic parameters,where the intrinsic parameters include focal length, optical center,radial distortion parameters, etc., and the extrinsic parameters includecamera location, camera orientation, etc. Camera extrinsic parameterscalibration typically involves determining a set of parameters thatrelate camera image coordinates to vehicle coordinates and vice versa.Some camera parameters, such as camera focal length, optical center,etc., are stable, while other parameters, such as camera orientation andposition, are not. For example, the height of the camera depends on theload of the vehicle, which will change from time to time.

In the known surround-view camera systems, the images from the camerasoverlap at the corners of the vehicle, where the camera calibrationprocess “stitches” the adjacent images together so that common elementsin the separate images directly overlap with each other to provide thedesired top-down view. During manufacture of the vehicle, these cameraimages are stitched together to provide this image using any of a numberof calibration techniques so that when the vehicle is first put intoservice, the cameras are properly calibrated. One calibration techniqueemployed is to position the vehicle on a checker-board pattern ofalternating light and dark squares where each point of the squares issuitably identified. Using these points in the developed images allowsthe camera calibration software to correct the position of the images sothat overlapping points in adjacent images are identified at the samelocation.

However, once the vehicle is put into service various things may occurthat could cause the orientation and position of the cameras to change,where the calibration of the camera includes errors causing misalignmentin the top-down image. These things may include loading of the vehiclethat causes camera position, such as height, and/or camera orientation,such as pitch, to change relative to world coordinates, small impacts tothe vehicle which may change the position and orientation of thecameras, etc. However, current video processing modules (VPM) thatprocess the images from the cameras to generate the top-down view areunable to recalibrate the cameras online while the vehicle is in use.Contrary, the vehicle operator must take the vehicle to a dealer orother authorized service center that has the ability to recalibrate thecameras in the same manner as was done during vehicle manufacture, whichhas obvious drawbacks.

SUMMARY OF THE INVENTION

The present disclosure describes a system and method for correcting thecalibration of a plurality of cameras on a mobile platform such as in asurround-view camera system on a vehicle based on changes in vehicledynamics. The method includes reading measurement values from one ormore sensors on the vehicle that identify a change in vehicle dynamicsand defining the plurality of cameras and a vehicle body as a singlereference coordinate system. The method also includes identifying themeasured values as a rotation matrix and a translation vector in thecoordinate system, and integrating the rotation matrix and thetranslation vector into a relationship between a vehicle coordinatesystem and a camera coordinate system to provide the calibrationcorrection for the cameras.

Additional features of the present invention will become apparent fromthe following description and appended claims, taken in conjunction withthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a vehicle including a surround-view camerasystem having multiple cameras;

FIG. 2 is an isometric view of a vehicle showing a coordinate system forworld coordinates, vehicle coordinates and camera coordinates;

FIG. 3 is a coordinate system showing a surround-view camera system in astationary position;

FIG. 4 is the coordinate system shown in FIG. 3 where the coordinatesystem has been changed as a result of a change in vehicle dynamics;

FIG. 5 is a representation of four raw images from four cameras for thesurround-view camera system showing matched feature pairs;

FIG. 6 is a block diagram of a system showing a process for matchingfeature points;

FIG. 7 is a representation of an image from a forward or rearwardlooking camera on a vehicle showing a horizon line;

FIG. 8 is an image from a forward or rearward looking camera on avehicle showing movement of the horizon line when the vehicle pitchesdown;

FIG. 9 is a representation of an image from a forward or rearwardlooking camera on a vehicle where the vehicle is in a rollconfiguration; and

FIG. 10 is a representation of an image from a forward or rearwardlooking camera on a vehicle showing a drift situation.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The following discussion of the embodiments of the invention directed toa system and method for correcting the calibration of a plurality ofcameras in a surround-view camera system on a vehicle that includesproviding corrections for vehicle dynamics as detected by sensors on thevehicle. For example, as discussed, the system and method has particularapplication for correcting the calibration of cameras on a vehicle.However, as will be appreciated by those skilled in the art, the systemand method may have application for other mobile platforms, such as ontrains, machines, tractors, boats, recreation vehicles, etc.

FIG. 1 is a top illustration of a vehicle 10 including a surround-viewcamera system having a front-view camera 12, a rear-view camera 14, aleft-side driver view camera 16 and a right-side passenger view camera18. The cameras 12-18 can be any camera suitable for the purposesdescribed herein, many of which are known in the automotive art, thatare capable of receiving light, or other radiation, and converting thelight energy to electrical signals in a pixel format using, for example,charged coupled devices (CCD). The cameras 12-18 generate frames ofimage data at a certain data frame rate that can be stored forsubsequent image processing in a video processing module (VPM) 20. Thecameras 12-18 can be mounted within or on any suitable structure that ispart of the vehicle 10, such as bumpers, facie, grill, side-viewmirrors, door panels, etc., as would be well understood and appreciatedby those skilled in the art. In one non-limiting embodiment, the sidecameras 16 and 18 are mounted under the side view mirrors and arepointed downwards.

The cameras 12-18 generate images of certain limited areas around thevehicle 10 that partially overlap. Particularly, area 24 is the imagearea for the camera 12, area 26 is the image area for the camera 14,area 28 is the image area for the camera 16, and area 30 is the imagearea for the camera 18, where area 32 is an overlap area of the images24 and 28, area 34 is an overlap area of the images 24 and 30, area 36is an overlap area of the images 28 and 26, and area 38 is an overlaparea of the images 30 and 26. Image data from the cameras 12-18 is sentto the VPM 20 that processes the image data to stitch the imagestogether that can then be displayed on a vehicle display as a singletop-down view image around the vehicle 10. Software algorithms are knownthat employ rotation matrices R and translation vectors t to orient andreconfigure the images from adjacent cameras so that the images properlyoverlap.

Vehicle dynamics, i.e., pitch, roll and height variation, influence theorientation of the images 24-30 from the cameras 12-18. For example, thevehicle 10 may pitch down during vehicle braking, may pitch up duringhard accelerations, may roll sideways during turns, may pitch up or downduring vehicle loading, etc., which may cause the images from thecameras 12-18 to change relative to each other even though the camerasare properly calibrated.

The present invention proposes a system and method for integratinginformation available from sensors on the vehicle about the vehicledynamics into the algorithm in the VPM 20 that determines thesurround-view image using the cameras 12-18 so the image can becorrected based on those changes to the vehicle dynamics. Generally, theproposed camera calibration correction technique defines threecoordinate systems, namely, a world coordinate system, a vehiclecoordinate system and a camera coordinate system each defined in anX-Y-Z positional orientation.

FIG. 2 is an illustration of a vehicle 50 including a camera 52 showingthese coordinates systems, where the subscript V represents the vehiclecoordinates, the subscript W represents the world coordinates and thesubscript C represents the camera coordinates. The vehicle 50 includes asensor 54 intended to represent all of the available sensors on anyparticular vehicle that can sense vehicle pitch, height variation androll and provide signals on the vehicle bus identifying vehicledynamics.

Equations (1)-(3) below represent the stationary orientation of thesurround-view camera system and equations (4)-(6) below identify theorientation of a dynamic position of the vehicle 50, where R_(dyn) isthe rotation dynamics matrix of the vehicle 50 in all of pitch, roll andheight variation. As is apparent, if the vehicle 50 is under somedynamic change, i.e., pitch, roll and height variation, the calculationof the point X_(C) in camera coordinates includes the translation vectort and the addition of the rotation matrix R for the dynamic change tothe vehicle 50.

$\begin{matrix}{{X_{V} = {X_{W} + t_{W\; 2V}^{\prime}}},} & (1) \\{{X_{Ci} = {{R_{V\; 2{Ci}}*X_{V}} + t_{V\; 2{Ci}}^{\prime}}},} & (2) \\{\mspace{34mu} {{= {{R_{V\; 2{Ci}}*X_{W}} + t_{W\; 2{Ci}}^{\prime}}},}} & (3) \\{{X_{V} = {R_{{dy}\; n}*\left( {X_{W} + t_{W\; 2V}^{\prime}} \right)}},} & (4) \\{{X_{C} = {{R_{V\; 2{Ci}}*X_{V}} + t_{V\; 2{Ci}}^{\prime}}},} & (5) \\{\mspace{34mu} {= {{R_{V\; 2{Ci}}*R_{{dy}\; n}*X_{W}} + {t_{W\; 2{Ci}}^{''}.}}}} & (6)\end{matrix}$

The cameras 12-18 that are part of the surround-view camera systemcombine with the vehicle body to form a rigid body frame. FIG. 3 is anillustration 150 of an X, Y and Z coordinate system including fourcameras 152 that are part of a surround-view camera system showing sucha combined reference frame. FIG. 4 shows the illustration 150 beingrotated in pitch and roll, where the rotation and translation vectorschange accordingly. For this coordinate system, the vehicle dynamics ofpitch, roll and height variation are defined by a rotation matrixR_(veh) and a translation vector T_(veh), where the rotation matrixR_(cam) and the translation vector T_(cam) of the cameras 152 arecorrected by the change in vehicle dynamics as:

(R _(cam) ,T _(cam))=f((R_(veh) ,T _(veh)),(R_(cam) ,T _(cam))_(stny)).  (7)

By providing such a correction to the orientation of the surround-viewcamera system, improved lane sensing, parking assist, etc. can beprovided.

The change in the relative orientation of the images from the cameras12-18 in the surround-view camera system from the calibration of thecameras 12-18 provided at vehicle manufacturer or at the dealer can beused to estimate the vehicle dynamics parameter R_(dyn), namely,rotation dynamics in two-degrees of freedom for pitch α and roll β ofthe vehicle 50, and translation dynamics in one-degree of freedom,namely, the height offset Δz of the vehicle 50. The present inventionalso proposes a system and method to estimate vehicle dynamics in thismanner that uses the overlap image area for any two of the cameras 12-18in the surround-view camera system to determine that common points inthe overlap image area are not at the same location, assuming thecameras are properly calibrated. The algorithm detects matching featurepoints (u, v) in the two images, and estimates three-degree of freedomvehicle dynamic parameters from the difference between the featurepoints (u, v). The matching feature points (u, v) in the images from twocameras are the projection of the same point location in worldcoordinates. Providing the matching feature points (u, v) for the samepoint from two cameras and solving the dynamic equations with theunknowns gives an estimate of the pitch α, roll β, and/or heightvariation of the vehicle 50 based on the distance between the points.

FIG. 5 is an illustration 80 of the vehicle 10 in a parking lot adjacentto other vehicles, where parking lot lines 78 can be used to identifymatching feature points (u, v) in two overlapping camera images. Thematching feature points (u, v) do not have to be on the ground, but canbe on any suitable object above ground or otherwise, such as on thevehicle body. For example, points 82 and 84 represent the same or commonpoint in the overlap area 32 for the images provided by the front camera12 and the left-side camera 16, respectively. Points 86 and 88 representthe same or common point in the overlap area 34 for the images from thecamera 12 and the right-side camera 18, respectively. Points 90 and 92represent the same or common point in the overlap area 36 between theleft-side camera 16 and the rear camera 14, respectively. Points 94 and96 represent the same or common point in the overlap area 38 between theright side camera 18 and the rear camera 14, respectively. The distancebetween the points 82 and 84, or the points 86 and 88, or the points 90and 92, or the points 94 and 96 caused by the change in vehicle dynamicsprovides the mechanism for determining that change in the vehicledynamics. Those skilled in the art will understand that many computervision and imaging systems employ feature point detection and matchingtechniques and algorithms, such as SIFT, SURF, ORB, etc., that may beapplicable for the purposes discussed herein.

FIG. 6 is a block diagram of a system 100 showing a generalrepresentation of the process for identifying the matching featurepoints (u, v) in the overlap areas 32, 34, 36 and 38. In the system 100,box 102 represents the image from the front camera 12, box 104represents the image from the left-side camera 16, box 106 representsthe image from the right-side camera 18, and box 108 represents theimage from the rear camera 14. A synchronization block 110 synchronizesthe timing of the images 102-108 from the cameras 12-18 so that all ofthe images 32, 34, 36 and 38 are aligned in time before being aligned inspace from the calibration process. The images 102 and 104 from thecameras 12 and 16, respectively, generate a region of interest 112 inthe overlap area 32, the images 102 and 106 from the cameras 12 and 18,respectively, generate a region of interest 114 in the overlap area 34,the images 104 and 108 from the cameras 16 and 14, respectively,generate a region of interest 116 in the overlap area 36, and the images106 and 108 from the cameras 14 and 18, respectively, generate a regionof interest 118 in the overlap area 38. The regions of interest 112,114, 116 and 118 are then provided to a processor 120 that identifiesthe several matching feature points (u, v) in the regions of interest112, 114, 116 and 118 in the manner as discussed herein.

Equations (8)-(12) below show this process for determining the rotationmatrix R_(dyn) and the translation vector t′_(W2V) that identify achange in the vehicle dynamics using the common matching feature points,which can then be used as an input to other vehicle systems, whereequation (8) shows the two feature points in the overlap image area thatshould be at the same location X when the cameras are calibrated.Suitable algorithms can be employed for this process, such as theLevenberg-Marquardt algorithm, gradient descent algorithms, etc.

(u, v)_(C1)˜(u, v)_(C2),   (8)

(u, v)_(C1)→X_(C1),   (9)

(u, v)_(C2)→X_(C2),   (10)

X _(C1) =R _(V2C1) *R _(dyn)*(X _(W) +t′ _(W2V))+t′ _(V2C1),   (11)

X _(C2) =R _(V2C2) *R _(dyn)*(X _(W) +t′ _(W2V))+t′ _(V2C2),   (12)

where (u, v)_(C1) is one feature point in an image from a first camerac1 that is a projection of world point X_(W), (u, v)_(C2) is anotherfeature point in an image from a second camera c2 that is a projectionof the world point X_(W), R_(V2Ci) is the rotation matrix of camera c₁in vehicle coordinates v, i is the index of the cameras, t is thetranslation vector, dyn represents dynamic, and w is world coordinates.

As discussed, the cameras 12-18 can be used to determine whether thevehicle 10 is either pitching, rolling or drifting relative to ahorizon. For example, FIG. 7 is a representation of an image 160 from acamera on the vehicle 10 traveling along a roadway 166 in front of thevehicle 10 and defining the non-dynamic horizon between air and groundby dotted line 162, where the vehicle 10 is not exhibiting roll, pitchor height variation. FIG. 8 is a representation of an image 168including the non-dynamic horizon line 162, but where the actual horizonis now at line 164 showing that the vehicle 10 has pitched down. FIG. 9is a representation of an image 170 of the roadway 166 where the horizonline 162 is shown angled in a roll direction. FIG. 10 is arepresentation an image 172 where the vehicle 10 has drifted on theroadway 166 in a left direction.

In addition, the process of determining the vehicle dynamics based onchanges in the surround-view image can include temporal tracking andsmoothing. The temporal tracking and smoothing can be provided fornon-transient vehicle change detection, such as a flat tire, badsuspension, towing and heavy load. Further, the tracking can beperformed to detect dangerous vehicle dynamic changes, such as roll overdetection and prevention, zigzag driving, etc. If the roll or pitchdynamics change aggressively anti-roll systems or other vehicle systemcan be notified to take corrective action.

As will be well understood by those skilled in the art, the several andvarious steps and processes discussed herein to describe the inventionmay be referring to operations performed by a computer, a processor orother electronic calculating device that manipulate and/or transformdata using electrical phenomenon. Those computers and electronic devicesmay employ various volatile and/or non-volatile memories includingnon-transitory computer-readable medium with an executable programstored thereon including various code or executable instructions able tobe performed by the computer or processor, where the memory and/orcomputer-readable medium may include all forms and types of memory andother computer-readable media.

The foregoing discussion disclosed and describes merely exemplaryembodiments of the present invention. One skilled in the art willreadily recognize from such discussion and from the accompanyingdrawings and claims that various changes, modifications and variationscan be made therein without departing from the spirit and scope of theinvention as defined in the following claims.

What is claimed is:
 1. A method for correcting a calibration of a plurality of cameras in a surround-view camera system on a vehicle, said method comprising: reading measurement values from one or more sensors on the vehicle that identify a change in vehicle dynamics; defining the plurality of cameras and a vehicle body as a single reference coordinate system; identifying the measured values as a rotation matrix and a translation vector in the reference coordinate system; and integrating the rotation matrix and the translation vector into a relationship between a vehicle coordinate system and a camera coordinate system to provide the correction of the camera calibration.
 2. The method according to claim 1 wherein integrating the rotation matrix and the translation vector into a relationship between a vehicle coordinate system and a camera coordinate system includes defining a stationary orientation of the surround-view camera system by: ${X_{V} = {X_{W} + t_{W\; 2V}^{\prime}}},\begin{matrix} {{X_{Ci} = {{R_{V\; 2{Ci}}*X_{V}} + t_{V\; 2{Ci}}^{\prime}}},} \\ {{= {{R_{V\; 2{Ci}}*X_{W}} + t_{W\; 2{Ci}}^{\prime}}},} \end{matrix}$ and defining a dynamic orientation of the vehicle by: ${X_{V} = {R_{{dy}\; n}*\left( {X_{W} + t_{W\; 2V}^{\prime}} \right)}},\begin{matrix} {{X_{C\;} = {{R_{V\; 2{Ci}}*X_{V}} + t_{V\; 2{Ci}}^{\prime}}},} \\ {{= {{R_{V\; 2{Ci}}*R_{{dy}\; n}*X_{W}} + t_{W\; 2{Ci}}^{''}}},} \end{matrix}$ where X is a sample point, i is a camera index, V is a designation for vehicle coordinates, W is a designation for reference coordinates, C is a designation for camera coordinates, dyn is a designation for vehicle dynamics in all of pitch, roll and height variation, R is the rotation matrix, and t is the translation vector.
 3. The method according to claim 2 wherein integrating the rotation matrix and the translation vector into a relationship between a vehicle coordinate system and a camera coordinate system to provide the camera calibration includes correcting the camera calibration using the equation: (R _(cam) ,T _(cam))=f((R _(veh) ,T _(veh)),(R _(cam) ,T _(cam))_(stny)), where stny is a designation for stationary, the vehicle dynamics of pitch, roll and height variation are defined by rotation matrix R_(veh) and translation vector T_(veh), R_(cam) is the rotation matrix in camera coordinates, and T_(cam) is the translation vector in camera coordinates.
 4. The method according to claim 1 wherein reading measurement values from one or more sensors includes reading measurement values from one or more sensors that provide vehicle or camera and vehicle body pitch, roll and height variation dynamics.
 5. The method according to claim 1 wherein the surround-view camera system includes four cameras, wherein a first camera is positioned at a front of the vehicle, a second camera is positioned at a back of the vehicle, a third camera is positioned at a left side of the vehicle and a fourth camera is positioned at a right side of the vehicle.
 6. The method according to claim 5 wherein images from the first camera overlap with images from the third camera, images from the first camera overlap with images from the fourth camera, images from the second camera overlap with images from the third camera, and images from the second camera overlap with images from the fourth camera.
 7. A method for correcting a calibration of a plurality of cameras on a mobile platform, said method comprising: reading measurement values from one or more sensors on the mobile platform that identify a change in platform dynamics; defining the plurality of cameras and a platform body as a single reference coordinate system; identifying the measured values as a rotation matrix and a translation vector in the reference coordinate system; and integrating the rotation matrix and the translation vector into a relationship between a platform coordinate system and a camera coordinate system to provide correction of the camera calibration.
 8. The method according to claim 7 wherein integrating the rotation matrix and the translation vector into a relationship between a platform coordinate system and a camera coordinate system includes defining a stationary orientation of the cameras by: ${X_{V} = {X_{W} + t_{W\; 2V}^{\prime}}},\begin{matrix} {{X_{{Ci}\;} = {{R_{V\; 2{Ci}}*X_{V}} + t_{V\; 2{Ci}}^{\prime}}},} \\ {{= {{R_{V\; 2{Ci}}*X_{W}} + t_{W\; 2{Ci}}^{\prime}}},} \end{matrix}$ and defining a dynamic orientation of the mobile platform by: ${X_{V} = {R_{{dy}\; n}*\left( {X_{W} + t_{W\; 2V}^{\prime}} \right)}},\begin{matrix} {{X_{C\;} = {{R_{V\; 2{Ci}}*X_{V}} + t_{V\; 2{Ci}}^{\prime}}},} \\ {{= {{R_{V\; 2{Ci}}*R_{{dy}\; n}*X_{W}} + t_{W\; 2{Ci}}^{''}}},} \end{matrix}$ where X is a sample point, i is a camera index, V is a designation for platform coordinates, W is a designation for reference coordinates, C is a designation for camera coordinates, dyn is a designation for platform dynamics in all of pitch, roll and height variation, R is the rotation matrix, and t is the translation vector.
 9. The method according to claim 8 wherein integrating the rotation matrix and the translation vector into a relationship between a platform coordinate system and a camera coordinate system to provide the camera calibration includes correcting the camera calibration using the equation: (R _(cam) ,T _(cam))=f((R _(veh) ,T _(veh)),(R _(cam) ,T _(cam))_(stny)), where stny is a designation for stationary, the platform dynamics of pitch, roll and heght variation are defined by rotation matrix R_(veh) and translation vector T_(veh), R_(cam) is the rotation matrix in camera coordinates, and T_(cam) is the translationa vector in camera coordinates.
 10. The method according to claim 7 wherein reading measurement values from one or more sensors includes reading measurement values from one or more sensors that provide mobile platform or camera and mobile platform pitch, roll and height variation dynamics.
 11. The method according to claim 7 wherein the plurality of cameras are part of a surround-view camera system.
 12. The method according to claim 11 wherein the surround-view camera system includes four cameras, wherein a first camera is positioned at a front of the platform, a second camera is positioned at a back of the platform, a third camera is positioned at a left side of the platform and a fourth camera is positioned at a right side of the platform.
 13. The method according to claim 12 wherein images from the first camera overlap with images from the third camera, images from the first camera overlap with images from the fourth camera, images from the second camera overlap with images from the third camera, and images from the second camera overlap with images from the fourth camera.
 14. A calibration system for correcting a calibration of a plurality of cameras in a surround-view camera system on a vehicle, said system comprising: means for reading measurement values from one or more sensors on the vehicle that identify a change in vehicle dynamics; means for defining the plurality of cameras and a vehicle body as a single reference coordinate system; means for identifying the measured values as a rotation matrix and a translation vector in the reference coordinate system; and means for integrating the rotation matrix and the translation vector into a relationship between a vehicle coordinate system and a camera coordinate system to provide the correction of the camera calibration.
 15. The calibration system according to claim 14 wherein the means for integrating the rotation matrix and the translation vector into a relationship between a vehicle coordinate system and a camera coordinate system defines a stationary orientation of the surround-view camera system by: ${X_{V} = {X_{W} + t_{W\; 2V}^{\prime}}},\begin{matrix} {{X_{{Ci}\;} = {{R_{V\; 2{Ci}}*X_{V}} + t_{V\; 2{Ci}}^{\prime}}},} \\ {{= {{R_{V\; 2{Ci}}*X_{W}} + t_{W\; 2{Ci}}^{''}}},} \end{matrix}$ and defines a dynamic orientation of the vehicle by: ${X_{V} = {R_{{dy}\; n}*\left( {X_{W} + t_{W\; 2V}^{\prime}} \right)}},\begin{matrix} {{X_{C\;} = {{R_{V\; 2{Ci}}*X_{V}} + t_{V\; 2{Ci}}^{\prime}}},} \\ {{= {{R_{V\; 2{Ci}}*R_{{dy}\; n}*X_{W}} + t_{W\; 2{Ci}}^{''}}},} \end{matrix}$ where X is a sample point, i is a camera index, V is a designation for vehicle coordinates, W is a designation for reference coordinates, C is a designation for camera coordinates, dyn is a designation for vehicle dynamics in all of pitch, roll and height variation, R is the rotation matrix, and t is the translation vector.
 16. The calibration system according to claim 15 wherein the means for integrating the rotation matrix and the translation vector into a relationship between a vehicle coordinate system and a camera coordinate system to provide the camera calibration corrects the camera calibration uses the equation: (R _(cam) ,T _(cam))=f((R _(veh) ,T _(veh)),(R_(cam) ,T _(cam))_(stny)), where stny is a designation for stationary, the vehicle dynamics of pitch, roll and height variation are defined by rotation matrix R_(veh) and translation vector T_(veh), R_(cam) is the rotation matrix in camera coordinates, and T_(cam) is the translation vector in camera coordinates.
 17. The calibration system according to claim 14 wherein the means for reading measurement values from one or more sensors reads measurement values from one or more sensors that provide vehicle or camera and vehicle body pitch, roll and height variation dynamics.
 18. The calibration system according to claim 14 wherein the surround-view camera system includes four cameras, wherein a first camera is positioned at a front of the vehicle, a second camera is positioned at a back of the vehicle, a third camera is positioned at a left side of the vehicle and a fourth camera is positioned at a right side of the vehicle.
 19. The calibration system according to claim 18 wherein images from the first camera overlap with images from the third camera, images from the first camera overlap with images from the fourth camera, images from the second camera overlap with images from the third camera, and images from the second camera overlap with images from the fourth camera. 